Missing Data and Measurement Errors: Statistical Approach to Incomplete Data Analysis

While collecting a complete dataset with no missing or inaccurate measurements is ideal, it is very rare due to a number of reasons. Incompleteness in data can introduce bias and/or information loss in estimating the relationship between the factors of interest and the outcome, potentially reducing the quality and validity of research findings. In epidemiological observational studies, these sources of bias have been increasingly widespread as the use of real-world data grows, which are not collected as planned, such as insurance claims databases and electronic medical records. This will increase the importance of controlling for the bias sources in statistical analyses. This article focuses on two important issues of incompleteness in data analysis: missing data and measurement error, and discusses statistical approaches to address them.